04_exploring.Rmd
Eduardo (with Dexter H Locke, PhD)
2024-05-20 21:16:41
General Outline (with objectives)
WHEN a) Year Map Overview (each colors based off of year, plus Shiny app (filter for years)) b) Stacked Bar Chart of Each Year (each season as different colors)
WHERE c) Three Jurisdictions Map Overview (different colors based off of the three tree giveaway distribution) d) Bar Chart (different colors based of the locations with count) e) Baltimore Neighborhood Map Overview (different colors based off of density of the plantings) f) Bar Chart Highlighting Top 10 Planted Neighborhoods (different colors based off of neighborhood), plus aggregating the rest as Other g) Census Tract Overview in Baltimore (different colors based off of density of the plantings) h) Census Tract Overview in Baltimore County (different colors based off of density of the plantings) i) Bar Graph highlighting Top 10 Locations Planted (Cleaned)
WHAT j) Map Overview of Top 10 Species (each species as a different color) k) Stacked Bar Chart (Highlighting the different species for each one) l) Map Overview of Top 5 Species each Season (each species as a different color, RShiny) m) Bar Graph of Top 5 Species for Each Year (each species as a color)
WHO l) Map Overview with Planting to Redline Zones (RShiny with Year) m) Map Overview with Planting to CBT Zones (RShiny with Year)
0 load libraries and read in data—-
# List all of your packages here
knitr::opts_chunk$set(echo = TRUE)
packs <-c(
'janitor' # cleans things up, also pipe-friendly cross-tabulations
, 'sf' # for spatial data support
, 'tidyverse' # cuz
, 'tidylog' # prints out what was done in dplyr and tidyr
, 'magrittr' # for the pipe
, 'mapview' # web maps for zooming and panning around
#, 'beepr' # makes noise when things are done!
, 'tictoc' # timing things.
, 'raster'
# , 'doParallel' # does what is says! PARALLEL
# 'broom.mixed',# tidiers for mixed models AND nlme::gls()
# , 'lubridate' # DATES!
, 'tidycensus' # tidy census package
, 'tidygeocoder' # geo coding
, 'leaflet' #creating the interactive mapping elements (more specific)
, 'shiny'
, 'leafsync' # linked maps
, 'DT'
)
# IF the packages in 'packs' are not already installed, install them
# OTHERWISE do NOTHING
if (length(setdiff(packs, rownames(installed.packages()))) > 0) {
install.packages(setdiff(packs, rownames(installed.packages())))
}
# lapply(packs, library, character.only = TRUE)
# this actually loads them with library(package_name)
vapply(packs, library, character.only = TRUE, logical(1), logical.return = TRUE, quietly = TRUE)## janitor sf tidyverse tidylog magrittr mapview
## TRUE TRUE TRUE TRUE TRUE TRUE
## tictoc raster tidycensus tidygeocoder leaflet shiny
## TRUE TRUE TRUE TRUE TRUE TRUE
## leafsync DT
## TRUE TRUE# # for tidycensus
# setting for get_acs
census_api_key('58fc555c77c229747ade7d9fe50e7c71297cf91a', install = TRUE, overwrite = TRUE)## [1] "58fc555c77c229747ade7d9fe50e7c71297cf91a"readRenviron("~/.Renviron")
options(tigris_use_cache = TRUE)## B get census data
vars21 <- tidycensus::load_variables('acs5', year = 2021)
# vars21 |> View()
my_vars <- c( 'pop' = 'B01001_001'
, 'medhhinc' = 'B19013_001'
)
(MD <-
tidycensus::get_acs(
geography = 'state'
, state = 'MD'
, variables = my_vars
, year = 2021
, geometry = TRUE
, output = 'wide'
, moe_level = 95
) |>
st_transform(crs = st_crs(4326)))## Getting data from the 2017-2021 5-year ACS## Simple feature collection with 1 feature and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -79.48765 ymin: 37.91172 xmax: -75.04894 ymax: 39.72304
## Geodetic CRS: WGS 84
## GEOID NAME popE popM medhhincE medhhincM
## 1 24 Maryland 6148545 NA 91431 679.1489
## geometry
## 1 MULTIPOLYGON (((-76.05015 3...mapview(MD)(cbg <-
tidycensus::get_acs(
geography = 'block group'
, state = 'MD'
, county = c('Baltimore City', 'Baltimore County')
, variables = my_vars
, year = 2021
, geometry = TRUE
, output = 'wide'
, moe_level = 95
) |>
st_transform(crs = st_crs(MD)) |>
mutate(jurisdiction = case_when(
str_like(GEOID, '24510%') ~ "Baltimore City",
str_like(GEOID, '24005%') ~ "Baltimore County",
TRUE ~ "Neither"
)))## Getting data from the 2017-2021 5-year ACS## mutate: new variable 'jurisdiction' (character) with 2 unique values and 0% NA## Simple feature collection with 1180 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -76.89556 ymin: 39.19541 xmax: -76.32143 ymax: 39.7212
## Geodetic CRS: WGS 84
## First 10 features:
## GEOID NAME
## 1 245100302001 Block Group 1, Census Tract 302, Baltimore city, Maryland
## 2 245100907003 Block Group 3, Census Tract 907, Baltimore city, Maryland
## 3 245102101002 Block Group 2, Census Tract 2101, Baltimore city, Maryland
## 4 245100702001 Block Group 1, Census Tract 702, Baltimore city, Maryland
## 5 245101508004 Block Group 4, Census Tract 1508, Baltimore city, Maryland
## 6 240054301042 Block Group 2, Census Tract 4301.04, Baltimore County, Maryland
## 7 240054008002 Block Group 2, Census Tract 4008, Baltimore County, Maryland
## 8 245101503003 Block Group 3, Census Tract 1503, Baltimore city, Maryland
## 9 245101507021 Block Group 1, Census Tract 1507.02, Baltimore city, Maryland
## 10 240054909001 Block Group 1, Census Tract 4909, Baltimore County, Maryland
## popE popM medhhincE medhhincM geometry
## 1 904 302.6383 48594 40679.83 MULTIPOLYGON (((-76.60643 3...
## 2 868 644.5957 55513 45449.36 MULTIPOLYGON (((-76.59894 3...
## 3 1577 411.0638 81944 19416.51 MULTIPOLYGON (((-76.63267 3...
## 4 986 676.7660 NA NA MULTIPOLYGON (((-76.58105 3...
## 5 1296 704.1702 45659 35710.13 MULTIPOLYGON (((-76.68275 3...
## 6 1440 735.1489 NA NA MULTIPOLYGON (((-76.64102 3...
## 7 904 270.4681 81172 31271.83 MULTIPOLYGON (((-76.73352 3...
## 8 252 128.6809 57672 49836.43 MULTIPOLYGON (((-76.65999 3...
## 9 1121 400.3404 44355 18785.02 MULTIPOLYGON (((-76.66609 3...
## 10 399 331.2340 NA NA MULTIPOLYGON (((-76.58763 3...
## jurisdiction
## 1 Baltimore City
## 2 Baltimore City
## 3 Baltimore City
## 4 Baltimore City
## 5 Baltimore City
## 6 Baltimore County
## 7 Baltimore County
## 8 Baltimore City
## 9 Baltimore City
## 10 Baltimore County# # examples / double checks
# mapview(baci, zcol = 'popE') +
# mapview( MD
# , color = 'black'
# , lwd = 1
# , alpha.regions = 0)
#
# pop_map <- mapview(baci, zcol = 'popE')
# inc_map <- mapview(baci, zcol = 'medhhincE')
#
# sync(pop_map, inc_map)
# county <-
# cbg |>
# summarize() #this is dissolve1 Reading in the data visually as a map and cleaning
# Tree Tribble
species_data <-
tribble(
~common_name, ~genus_species, ~itree,
"Hackberry", "Celtis occidentalis", "CEOC",
"Willow Oak", "Quercus phellos", "QUPH",
"Black Gum", "Nyssa sylvatica", "NYSY",
"Eastern Redbud", "Cercis canadensis", "CECA",
"Sassafras", "Sassafras albidum", "SAAL",
"Chestnut Oak", "Quercus prinus", "QUPR",
"Eastern Red Cedar", "Juniperus virginiana", "JUVI",
"Bald Cypress", "Taxodium distichum", "TADI",
"Black Oak", "Quercus velutina", "QUVE",
"American Holly", "Ilex opaca", "ILOP",
"Sweetbay Magnolia", "Magnolia virginiana", "MAVI",
"American Sweetgum", "Liquidambar styraciflua", "LIST",
"Bur Oak", "Quercus macrocarpa", "QUMA1",
"Black Chokeberry", "Aronia melanocarpa", "ARME6",
"Blackhaw Viburnum", "Viburnum prunifolium", "VIPR",
"Gingko", "Ginkgo biloba", "GIBI",
"American Persimmon", "Diospyros virginiana", "DIVI",
"Flowering Cherry", "Prunus yedoensis", "PRYE",
"Post Oak", "Quercus stellata", "QUST",
"Chinkapin Oak", "Quercus muehlenbergii", "QUMU",
"Tulip Poplar", "Liriodendron tulipifera", "LITU",
"Shortleaf Pine", "Pinus echinata", "PIEC",
"Eastern White Pine", "Pinus americana", "PIST",
"Yellow Birch", "Betula alleghaniensis", "BEAL2",
"American Yellowwood","Cladrastis luteau", "CLLU",
"Smooth Service Berry","Amelanchier laevis", "AMLA",
"Boxelder Maple", "Acer negundo", "ACNE",
"American Linden", "Tilia americana", "TIAMRE",
"American Plum", "Prunus americana", "PRAM"
)
# Loading data
bwb_total_giveaway <-
read_csv("output_data/bwb_giveaway_total_final_2024-05-13.csv") |>
filter(!is.na(lat)) |> #Good double check
st_as_sf(coords = c("long","lat"), crs = 4326) |>
st_join(cbg) |>
mutate(common_name = case_when(
common_name == "Boxelder" ~ "Boxelder Maple",
common_name == "Yoshino Flowering Cherry" ~ "Flowering Cherry",
common_name == "Black Tupelo" ~ "Black Gum",
common_name == "Chinquapin" ~ "Chinkapin Oak",
common_name == "Breakfast" ~ "Other",
common_name == "Blackhaw Vibernum" ~ "Blackhaw Viburnum",
common_name == "Ginkgo" ~ "Gingko",
TRUE ~ common_name
)) |>
filter(address != "822 Montpellier") |>
left_join(species_data, by = "common_name") |>
mutate(
genus_species = coalesce(genus_species.x, genus_species.y),
itree = coalesce(itree.x, itree.y)
) |>
dplyr::select(-genus_species.x, -genus_species.y, -itree.x, -itree.y) |>
left_join(species_data, by = "genus_species") |>
mutate(
common_name = coalesce(common_name.x, common_name.y),
itree = coalesce(itree.x, itree.y)
) |>
dplyr::select(-common_name.x, -common_name.y, -itree.x, -itree.y) |>
mutate(genus_species = ifelse(common_name == "Other", "Other", genus_species),
itree = ifelse(common_name == "Other", NA_character_, itree))2 Creating multiple maps with corresponding graph
A General Map
3 Performing the a-spatial double checks
A Time
# Playing with year (result is season wide)
bwb_total_giveaway |>
st_drop_geometry() |>
tabyl(year, season) |>
as_tibble() |>
DT::datatable()# Same data, different shape (result is time long)
(giveaway_counts_season_year <-
bwb_total_giveaway |>
st_drop_geometry() |>
group_by(year, season) |>
count())## group_by: 2 grouping variables (year, season)## count: now 20 rows and 3 columns, 2 group variables remaining (year, season)## # A tibble: 20 × 3
## # Groups: year, season [20]
## year season n
## <dbl> <chr> <int>
## 1 2013 Fall 43
## 2 2013 Spring 86
## 3 2014 Fall 111
## 4 2014 Spring 107
## 5 2015 Fall 278
## 6 2015 Spring 192
## 7 2016 Fall 256
## 8 2016 Spring 308
## 9 2017 Fall 399
## 10 2017 Spring 720
## 11 2018 Fall 255
## 12 2018 Spring 1274
## 13 2019 Fall 531
## 14 2019 Spring 1186
## 15 2021 Fall 440
## 16 2021 Spring 564
## 17 2022 Fall 426
## 18 2022 Spring 533
## 19 2023 Fall 562
## 20 2023 Spring 731# Graphing this giveaway counts
giveaway_counts_season_year |>
ggplot(aes(x = year, y = n, color = season)) +
geom_point() +
# geom_line() +
geom_smooth(method = "lm") +
geom_smooth()## `geom_smooth()` using formula = 'y ~ x'## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
#geom_sf is for vector spatial data, data is the first argument, matters when you have layers. In terms of the ggplot, aes defines the conditions of the graph (let's you pass it through each point). geom_point makes the point graph. ggplot can be seen as global
#NULL Lets you always rull
# Graph about time
giveaway_counts_season_year |>
ggplot(aes(x = as.factor(year), y = n, color = season, group = season)) +
geom_point() +
geom_line() +
labs(
title = "Number of Tree Giveaways by Year"
, subtitle = "The Blue Water Baltimore data is also looking at specific seasons"
, caption = "Testing"
, x = "Year"
, y = "Number of Trees Given Away"
) +
theme_bw(16) +
theme(legend.position = c(.1, .7)) +
NULL
B Location
glimpse(bwb_total_giveaway)## Rows: 9,002
## Columns: 24
## $ giveaway_location <chr> "Winand ES", "Winand ES", "Winand ES", "Winand ES", …
## $ year <dbl> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013…
## $ season <chr> "Spring", "Spring", "Spring", "Spring", "Spring", "S…
## $ date <dttm> 2013-04-25, 2013-04-25, 2013-04-25, 2013-04-25, 201…
## $ address <chr> "4505 Hawksbury Road", "4708 Three Oaks Road", "4708…
## $ city <chr> "Pikesville", "Pikesville", "Pikesville", "Owings Mi…
## $ state <chr> "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD"…
## $ zip_code <dbl> 21208, 21208, 21208, 21133, 21208, 21208, 21208, 211…
## $ county <chr> "Baltimore County", "Baltimore County", "Baltimore C…
## $ first_name <chr> "Karen", "Makayla", "Makayla", "Al", "Balinda", "Bal…
## $ last_name <chr> "James", "Hill", "Hill", "Brown", "Oliver", "Oliver"…
## $ phone <chr> "410-602-2089", "410-963-6512", "410-963-6512", "678…
## $ email <chr> "koceysmommie@aolo.com", "panhill2000@yahoo.com", "p…
## $ geometry <POINT [°]> POINT (-76.75174 39.37312), POINT (-76.7598 39…
## $ GEOID <chr> "240054026041", "240054026042", "240054026042", "240…
## $ NAME <chr> "Block Group 1, Census Tract 4026.04, Baltimore Coun…
## $ popE <dbl> 4172, 2304, 2304, 2078, 4172, 4172, 4172, 2637, 2504…
## $ popM <dbl> 1196.2553, 906.7234, 906.7234, 899.5745, 1196.2553, …
## $ medhhincE <dbl> 102434, 108264, 108264, 78359, 102434, 102434, 10243…
## $ medhhincM <dbl> 37537.87, 28624.34, 28624.34, 55193.36, 37537.87, 37…
## $ jurisdiction <chr> "Baltimore County", "Baltimore County", "Baltimore C…
## $ genus_species <chr> "Amelanchier sp.", "Quercus phellos", "Amelanchier s…
## $ common_name <chr> "Serviceberry", "Willow Oak", "Serviceberry", "Willo…
## $ itree <chr> "AM", "QUPH", "AM", "QUPH", "QUPH", "QURU", "QUPH", …bwb_total_giveaway |>
st_drop_geometry() |>
tabyl(giveaway_location)## giveaway_location n percent valid_percent
## 32nd Street Farmers Market 168 0.0186625194 0.0340149828
## Anneslie 12 0.0013330371 0.0024296416
## Cherry Hill Urban Garden 24 0.0026660742 0.0048592833
## Chestnut Hill 4 0.0004443457 0.0008098805
## Cromwell Valley Harvest Festival 62 0.0068873584 0.0125531484
## Cromwell Valley Park 43 0.0047767163 0.0087062158
## Forestry Nursery 61 0.0067762719 0.0123506783
## GFLP 38 0.0042212842 0.0076938652
## Gateway Park at Darley Park 14 0.0015552100 0.0028345819
## Greektown - John Ruhrah EMS 55 0.0061097534 0.0111358575
## Gwynns Falls Leakin Park 63 0.0069984448 0.0127556185
## Gwynns Falls/Leakin Park 114 0.0126638525 0.0230815955
## Herring Run Nursery 1260 0.1399688958 0.2551123709
## JFX Farmers Market 146 0.0162186181 0.0295606398
## Langston Hughes Community Center 17 0.0018884692 0.0034419923
## REI Timonium 90 0.0099977783 0.0182223122
## Ridgeleigh 9 0.0009997778 0.0018222312
## Ridgely Manor Park 354 0.0393245945 0.0716744280
## Rogers Forge 8 0.0008886914 0.0016197611
## St Matthews Catholic Church 32 0.0035547656 0.0064790443
## Stillmeadow Community Fellowship 2152 0.2390579871 0.4357157319
## Stoneleigh 8 0.0008886914 0.0016197611
## Washington Hill Fall Community Day 5 0.0005554321 0.0010123507
## West Towson 87 0.0096645190 0.0176149018
## Westy Walk 79 0.0087758276 0.0159951407
## Winand ES 34 0.0037769385 0.0068839846
## <NA> 4063 0.4513441457 NAC Species
#Genus Species
bwb_total_giveaway |>
st_drop_geometry() |>
tabyl(genus_species) ## genus_species n percent
## Acer negundo 8 0.0008886914
## Acer rubrum 110 0.0122195068
## Acer saccharinum 6 0.0006665186
## Acer saccharum 18 0.0019995557
## Aesculus hippocastanum 1 0.0001110864
## Aesculus parviflora 1 0.0001110864
## Aesculus pavia 29 0.0032215063
## Alnus serrulata 12 0.0013330371
## Amelanchier canadensis 164 0.0182181737
## Amelanchier laevis 16 0.0017773828
## Amelanchier sp. 648 0.0719840036
## Aronia melanocarpa 48 0.0053321484
## Asimina triloba 544 0.0604310153
## Betula alleghaniensis 36 0.0039991113
## Betula lenta 5 0.0005554321
## Betula nigra 206 0.0228838036
## Carpinus caroliniana 295 0.0327704954
## Carya glabra 34 0.0037769385
## Carya illinoinensis 33 0.0036658520
## Carya laciniosa 22 0.0024439014
## Casuarina equisetifolia 25 0.0027771606
## Celtis occidentalis 95 0.0105532104
## Cercis canadensis 300 0.0333259276
## Chamaecyparis thyoides 9 0.0009997778
## Chionanthus virginicus 57 0.0063319262
## Cladrastis kentuckea 55 0.0061097534
## Cladrastis kentukea 36 0.0039991113
## Cladrastis luteau 5 0.0005554321
## Cornus alterniflora 3 0.0003332593
## Cornus alternifolia 34 0.0037769385
## Cornus florida 198 0.0219951122
## Corylus americana 162 0.0179960009
## Crataegus phaenopyrum 5 0.0005554321
## Diospyros viginiana 57 0.0063319262
## Diospyros virginiana 380 0.0422128416
## Fagus grandifolia 48 0.0053321484
## Ficus carica 325 0.0361030882
## Ginkgo biloba 92 0.0102199511
## Gymnocladus dioicus 1 0.0001110864
## Gymnocladus dioicusa 30 0.0033325928
## Hamamelis virginiana 177 0.0196622973
## Ilex opaca 307 0.0341035325
## Juniperus virginiana 170 0.0188846923
## Lagerstroemia sp. 1 0.0001110864
## Liquidambar styraciflua 66 0.0073317041
## Liriodendron tulipifera 87 0.0096645190
## Magnolia grandiflora 45 0.0049988891
## Magnolia virginiana 546 0.0606531882
## Nyssa sylvatica 301 0.0334370140
## Ostrya virginiana 19 0.0021106421
## Other 47 0.0052210620
## Pinus echinata 26 0.0028882471
## Pinus rigida 49 0.0054432348
## Pinus strobus 135 0.0149966674
## Pinus taeda 74 0.0082203955
## Pinus virginiana 49 0.0054432348
## Platanus hybrida 2 0.0002221729
## Platanus occidentalis 180 0.0199955565
## Populus deltoides 134 0.0148855810
## Prunus americana 322 0.0357698289
## Prunus angustifolia 25 0.0027771606
## Prunus maritima 9 0.0009997778
## Prunus serotina 106 0.0117751611
## Prunus serrulata 4 0.0004443457
## Prunus yedoensis 4 0.0004443457
## Quercus alba 215 0.0238835814
## Quercus bicolor 131 0.0145523217
## Quercus coccinea 33 0.0036658520
## Quercus falcata 85 0.0094423461
## Quercus ilicifolia 1 0.0001110864
## Quercus macrocarpa 125 0.0138858032
## Quercus marilandica 2 0.0002221729
## Quercus michauxii 48 0.0053321484
## Quercus montana 19 0.0021106421
## Quercus muehlenbergii 51 0.0056654077
## Quercus palustris 38 0.0042212842
## Quercus phellos 185 0.0205509887
## Quercus prinus 67 0.0074427905
## Quercus rubra 110 0.0122195068
## Quercus stellata 30 0.0033325928
## Quercus velutina 27 0.0029993335
## Robinia pseudoacacia 6 0.0006665186
## Salix nigra 1 0.0001110864
## Sambucus canadensis 253 0.0281048656
## Sambucus nigra 26 0.0028882471
## Sassafras albidum 168 0.0186625194
## Taxodium distichum 233 0.0258831371
## Thuja occidentalis 3 0.0003332593
## Tilia americana 47 0.0052210620
## Ulmus americana 8 0.0008886914
## Viburnum prunifolium 51 0.0056654077
## prunus_americana 1 0.0001110864#Common Name
(giveaway_counts_common_name <-
bwb_total_giveaway |>
st_drop_geometry() |>
tabyl(common_name) |>
as_tibble() |>
arrange(desc(n))
)## # A tibble: 84 × 3
## common_name n percent
## <chr> <int> <dbl>
## 1 Serviceberry 812 0.0902
## 2 Sweetbay Magnolia 546 0.0607
## 3 Pawpaw 544 0.0604
## 4 American Persimmon 437 0.0485
## 5 Common Fig 325 0.0361
## 6 American Plum 323 0.0359
## 7 American Holly 307 0.0341
## 8 Black Gum 301 0.0334
## 9 Eastern Redbud 300 0.0333
## 10 American Hornbeam 295 0.0328
## # ℹ 74 more rowsbwb_total_giveaway |>
filter(is.na(common_name) | is.na(genus_species))## filter: removed all rows (100%)## Simple feature collection with 0 features and 23 fields
## Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
## Geodetic CRS: WGS 84
## # A tibble: 0 × 24
## # ℹ 24 variables: giveaway_location <chr>, year <dbl>, season <chr>,
## # date <dttm>, address <chr>, city <chr>, state <chr>, zip_code <dbl>,
## # county <chr>, first_name <chr>, last_name <chr>, phone <chr>, email <chr>,
## # geometry <GEOMETRY [°]>, GEOID <chr>, NAME <chr>, popE <dbl>, popM <dbl>,
## # medhhincE <dbl>, medhhincM <dbl>, jurisdiction <chr>, genus_species <chr>,
## # common_name <chr>, itree <chr>#Making that
bwb_total_giveaway |>
filter(is.na(common_name) & is.na(genus_species))## filter: removed all rows (100%)## Simple feature collection with 0 features and 23 fields
## Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
## Geodetic CRS: WGS 84
## # A tibble: 0 × 24
## # ℹ 24 variables: giveaway_location <chr>, year <dbl>, season <chr>,
## # date <dttm>, address <chr>, city <chr>, state <chr>, zip_code <dbl>,
## # county <chr>, first_name <chr>, last_name <chr>, phone <chr>, email <chr>,
## # geometry <GEOMETRY [°]>, GEOID <chr>, NAME <chr>, popE <dbl>, popM <dbl>,
## # medhhincE <dbl>, medhhincM <dbl>, jurisdiction <chr>, genus_species <chr>,
## # common_name <chr>, itree <chr>D Making the count by city
(giveaway_counts_city_season <-
bwb_total_giveaway |>
st_drop_geometry() |>
group_by(city, year, season) |>
count() |>
as_tibble() |>
arrange(desc(n))
)## group_by: 3 grouping variables (city, year, season)## count: now 349 rows and 4 columns, 3 group variables remaining (city, year, season)## # A tibble: 349 × 4
## city year season n
## <chr> <dbl> <chr> <int>
## 1 Baltimore 2018 Spring 1125
## 2 Baltimore 2019 Spring 1067
## 3 Baltimore 2023 Spring 665
## 4 Baltimore 2019 Fall 531
## 5 Baltimore 2023 Fall 528
## 6 Baltimore 2017 Spring 466
## 7 Baltimore 2017 Fall 317
## 8 Baltimore 2021 Spring 297
## 9 Baltimore 2022 Spring 262
## 10 Baltimore 2021 Fall 247
## # ℹ 339 more rowsgiveaway_counts_city_season |>
ggplot(aes(year, n, color = season, group = season)) +
geom_point() +
geom_line() +
facet_wrap(~city) +
theme_bw(16) +
NULL## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
##
bwb_total_giveaway |>
st_drop_geometry() |>
group_by(zip_code, year, season) |>
count() |>
as_tibble() |>
arrange(desc(n)) |>
ggplot(aes(year, n, color = season, group = season)) +
geom_point() +
geom_line() +
facet_wrap(~zip_code) +
theme_bw(16) +
NULL## group_by: 3 grouping variables (zip_code, year, season)
## count: now 765 rows and 4 columns, 3 group variables remaining (zip_code, year, season)
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Comparing to county data
bwb_total_giveaway |>
st_drop_geometry() |>
group_by(county, year, season) |>
count() |>
as_tibble() |>
arrange(desc(n)) |>
ggplot(aes(year, n, color = season, group = season)) +
geom_point() +
geom_line() +
facet_wrap(~county) +
theme_bw(16) +
theme(axis.text.x = element_text(angle = 90, vjust = .5)) +
NULL## group_by: 3 grouping variables (county, year, season)
## count: now 57 rows and 4 columns, 3 group variables remaining (county, year, season)
4 maps and graphs to make
# Species over time
# graph with time on horizontal
# vertical is amount
# color for season
# lines per species (too many)
bwb_total_giveaway |>
st_drop_geometry() |>
group_by(year, season, common_name) |>
count() |> # summary() # n ranges from 1 to 192
filter(n > 10) |>
ggplot(aes(year, n, group = common_name, color = season)) +
geom_point() +
geom_line() +
facet_wrap(~common_name)## group_by: 3 grouping variables (year, season, common_name)## count: now 452 rows and 4 columns, 3 group variables remaining (year, season, common_name)## filter (grouped): removed 209 rows (46%), 243 rows remaining## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
# map for the top five species
five_most_common_species <-
bwb_total_giveaway |>
st_drop_geometry() |>
group_by(common_name) |>
count() |>
ungroup() |>
arrange(desc(n)) |>
slice(1:10)## group_by: one grouping variable (common_name)
## count: now 84 rows and 2 columns, one group variable remaining (common_name)
## ungroup: no grouping variables
## slice: removed 74 rows (88%), 10 rows remainingbwb_total_giveaway |>
filter(common_name %in% five_most_common_species$common_name) |>
mapview()## filter: removed 4,812 rows (53%), 4,190 rows remainingbwb_total_giveaway |>
filter(common_name %in% five_most_common_species$common_name) |>
mapview(zcol = 'common_name')## filter: removed 4,812 rows (53%), 4,190 rows remaining